Recreating a worm brain with a series of interconnected computational nodes, referred to as neural networks, is often an exercise in minimalism. Most recently, a team of researchers led by Ramin Hasani at TU Wein in Vienna succeeded in training a network made up of just 12 digital neurons—loosely inspired by the well-understood “tap withdrawal” neural circuit in C. elegans, which controls a physical reflex to touch—to park a tiny RC car outfitted with sensors.

“Because these circuits are small, they’re more interpretable because we can quantify the contribution of neurons to the output much better than other systems that are out there,” Hasani told me over the phone. “If you have machine learning models that are closer to the capacity of natural learning systems, then one of the main advantages is going to be more transparency and control.”

An interesting byproduct of AI that’s inspired by biology is being able to compare the two to see how similar they really are. “We tried to look into the circuits after training on a real-life task to try and understand if there is a relation between the actual worm and these circuits that are working on parking the car,” Hasani said. Curiously, both the AI model and the real C. elegans neural circuit contained two neurons that seemed to be acting antagonistically, he said—when one was highly active, the other wasn’t.